What Google Translate can tell us about vibecoding

LLMs vs Google Translate and DeepL

• Several commenters argue the article’s focus on Google Translate is outdated: DeepL and modern LLMs produce much better, more nuanced translations.
• Others note Google already uses neural and LLM-style models in some products, but quality still trails alternatives in many cases.

Context, Tone, and Translation Workflows

• Experienced translators report LLMs can handle tone, politeness, and cultural nuance well if given enough context and carefully designed prompts.
• Some describe multi-step systems combining multiple models, asking the user about intent (literal vs free, footnotes, target culture), then synthesizing and iteratively refining drafts.
• Critics point out these workflows still require expert oversight; they accelerate professionals but are not turnkey solutions for laypeople.

Impact on Translators’ Jobs

• There is disagreement: some say Google Translate did not destroy translation work; others say LLMs plus DeepL are now causing real contraction, especially for routine commercial jobs.
• Consensus emerges that high-stakes domains (law, government, literature, interpreting) will retain humans longer, but much “ordinary” translation is shifting to post‑editing AI output, often at lower pay.

Parallels to Software Engineering and “Vibecoding”

• Many see translation as an analogy to AI coding assistants: useful accelerants for experts, not full replacements—for now.
• Some expect downward pressure on junior developer jobs and wages as “vibe coders” and non‑specialists can produce superficially working software.
• Others argue increased productivity historically leads to more software and more maintenance work, not fewer engineers, though there’s concern about an explosion of low‑quality code.

Localization, Culture, and Nuance

• Discussion highlights how real translation/localization involves idioms, cultural references, value-laden concepts (e.g., “freedom”), and matching performance constraints (e.g., dubbing lip-sync).
• Examples from Pixar, anime, and children’s textbooks show tensions between preserving foreign culture vs adapting to local familiarity.

Reliability, Safety, and Evaluation

• Commenters stress that non‑experts often cannot evaluate translations or AI‑generated code; outputs may “run” or read fluently yet be subtly wrong.
• Techniques like round‑trip translation help but miss many semantic and register errors.
• Concerns are raised about misclassification (Chinese vs Japanese), policy refusals, and serious failures such as mistranslating insults into racial slurs.

Debate Over the Article’s Examples and Claims

• Some challenge the article’s Norwegian “potatoes” politeness example as linguistically inaccurate and see the setup as a straw man about both translation and AI risk.
• Others praise the broader conclusion: current AI is powerful but still weak on deep context and ambiguity, and talk of total professional displacement is premature.